Large Language Models have been successful in a wide variety of Natural
Language Processing tasks by capturing the compositionality of the text
representations. In spite of their great success, these vector representations
fail to capture meaning of idiomatic multi-word expressions (MWEs). In this
paper, we focus on the detection of idiomatic expressions by using binary
classification. We use a dataset consisting of the literal and idiomatic usage
of MWEs in English and Portuguese. Thereafter, we perform the classification in
two different settings: zero shot and one shot, to determine if a given
sentence contains an idiom or not. N shot classification for this task is
defined by N number of common idioms between the training and testing sets. In
this paper, we train multiple Large Language Models in both the settings and
achieve an F1 score (macro) of 0.73 for the zero shot setting and an F1 score
(macro) of 0.85 for the one shot setting. An implementation of our work can be
found at
this https URL.

本文采用二分法进行假语汇的检测，使用由英语和葡萄牙语中字面和惯用用法的 Mult-word expressions 组成的数据集，在零 - shot 和一 - shot 两种不同的情境下执行分类，并使用多个 LLMs 进行训练，其在零 - shot 设置下的 F1 得分（宏）为 0.73，在一 - shot 设置下的 F1 得分（宏）为 0.85。

JARVix 参加 SemEval-2022 任务 2：了解一个人需要一个人吗？使用零阶和一阶学习进行成语性检测

JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot Learning

In recent years, pretrained language models have revolutionized the NLP
world, while achieving state of the art performance in various downstream
tasks. However, in many cases, these models do not perform well when labeled
data is scarce and the model is expected to perform in the zero or few shot
setting. Recently, several works have shown that continual pretraining or
performing a second phase of pretraining (inter-training) which is better
aligned with the downstream task, can lead to improved results, especially in
the scarce data setting. Here, we propose to leverage sentiment-carrying
discourse markers to generate large-scale weakly-labeled data, which in turn
can be used to adapt language models for sentiment analysis. Extensive
experimental results show the value of our approach on various benchmark
datasets, including the finance domain. Code, models and data are available at
this https URL.

本文研究使用情感搭载的话语标记产生大规模弱标记数据并利用此数据训练与情感分析更好契合的连续预训练模型，以提高零标记和少样本情况下的性能表现，并在多项基准数据集上进行了广泛的实验，包括金融领域。